ShiftDev 2020, Split Croatia. One of the hottest topics in database land these days is BigQuery ML. A new way to use machine learning on top of tabular data straight on your tables without leaving the query editor. With BigQuery ML, you can build machine learning models without leaving the database environment and training it on massive datasets. In this demo session, we are going to demonstrate common marketing Machine Learning use cases how to build, train, eval and predict, your own scalable machine learning models using SQL language. The audience will get first hand experience how to write CREATE MODEL sql syntax to build machine learning models such as: Multiclass logistic regression for classification, K-means clustering, Matrix factorization, ARIMA time series predictions, Import TensorFlow models for prediction in BigQuery. Models are trained and accessed in BigQuery using SQL — a language data analysts know. This enables business decision making through predictive analytics across the organization without leaving the query editor.

1. Applying BigQueryML
on E-commerce Data Analytics
September 2020 - Split Croatia
Márton Kodok / @martonkodok
Google Developer Expert at REEA.net

2. ● Among the Top3 romanians on Stackoverflow 175k reputation
● Google Developer Expert on Cloud technologies
● Crafting Web/Mobile backends at REEA.net
● BigQuery + Redis database engine expert
Slideshare: martonkodok
Twitter: @martonkodok
StackOverflow: pentium10
GitHub: pentium10
Applying BigQuery ML on E-commerce Data Analytics @martonkodok
About me

3. 1. E-commerce Workloads and data models
2. What is BigQuery? - Data warehouse in the Cloud
3. Introduction to BigQuery ML - execute ML models using SQL
4. Practical use cases
5. Predict, recommend and forecastwith BigQuery ML
6. Conclusions
Agenda
Applying BigQuery ML on E-commerce Data Analytics @martonkodok

4. Shop - products, tagging, features, attributes
Users profile, preferences, favorites, rating, engagement
Customers orders, re-orders, profile, associated products, survey, feedback, 360°
Analytics metrics, event data, page hits, email campaigns, A/B split tests
Upsells recommendations, price tags, strategy, discounts, vouchers
Enriched data sku, sentiment analysis, image parsing, object recognition
E-commerce Workloads and data models
Applying BigQuery ML on E-commerce Data Analytics @martonkodok

5. Shop - products, tagging, features, attributes
Users profile, preferences, favorites, rating, engagement
Customers orders, re-orders, profile, associated products, survey, feedback, 360°
Analytics metrics, event data, page hits, email campaigns, A/B split tests
Upsells recommendations, price tags, strategy, discounts, vouchers
Enriched data sku, sentiment analysis, image parsing, object recognition
E-commerce Workloads and data models
Applying BigQuery ML on E-commerce Data Analytics @martonkodok

6. “ Where to store all these
rawdata?
Applying BigQuery ML on E-commerce Data Analytics @martonkodok

7. BigQuery
On-Premises Servers
ApplicationEvents
Frontend
Metrics / Logs/
Streaming
Applying BigQuery ML on E-commerce Data Analytics @martonkodok
SQL

8. Analytics-as-a-Service - Data Warehouse in the Cloud
Familiar DB Structure (table, columns, views, struct, nested, JSON)
Decent pricing (storage: $20/TB cold: $10/TB,queries $5/TB) *Sep 2020
SQL 2011 + Javascript UDF (User Defined Functions)
BigQuery ML enables users to create machine learning models by SQL queries
Scales into Exabytes on Managed Infrastructure
Integrates with Cloud SQL + Cloud Storage + Sheets + Pub/Sub connectors
What is BigQuery?
Applying BigQuery ML on E-commerce Data Analytics @martonkodok

9. 1. Load from file - either local or from GCS (max 5TB each)
2. Streaming rows - event driven approach - high throughput 1M rows/sec
3. Functions - observer-trigger based (Google Cloud Functions)
4. Join with Cloud SQL - Ability to join with MySQL, Postgres
5. Pipelines - flexibility to do ETL - FluentD, Kafka, Google Dataflow
6. Export from connected services - Firestore, Billing, AuditLogs, Stackdriver
7. Firebase - Analytics - Messaging - Crashlytics - Perf. Monitoring - Predictions
Loading Data into BigQuery
Applying BigQuery ML on E-commerce Data Analytics @martonkodok

10. “ We have our app outside of GCP.
We need to join with our SQL database.
Solution: EXTERNAL_QUERY
Applying BigQuery ML on E-commerce Data Analytics @martonkodok

11. Combine on-premise with Cloud
App
Load
Balancing
NGINX
Compute Engine
10GB PD
2 1
Database Service (Master/Slave)
Compute Engine
10GB PD
4 1
Compute Engine
10GB PD
4 1
Compute Engine
10GB PD
4 1
BigQuery
Applying BigQuery ML on E-commerce Data Analytics @martonkodok
Zone 1
us-east1-a
Replica
Cloud SQL
Cloud
VPN
Gateway
Execute combined
queries
Report

12. EXTERNAL_QUERY: Run in BQ a query from Cloud SQL db
Applying BigQuery ML on E-commerce Data Analytics @martonkodok

13. ● SQL language to run BigData queries for everyday Devs
● run raw ad-hoc queries (either by analysts/sales or Devs)
● no more throwing away-, expiring-, aggregating old data
● it’s serverless
● no provisioning/deploy
● no running out of resources
● no more focus on large scale execution plan
Our benefits
Applying BigQuery ML on E-commerce Data Analytics @martonkodok

14. What is BigQueryML?
Applying BigQuery ML on E-commerce Data Analytics @martonkodok

15. BigQuery ML
1. CREATE MODEL in SQL to increase
development speed
2. Predict, recommend, foreast on tabular
data with SQL
3. Automate common ML tasks and
hyperparameter tuning by creating new
models as easy ascreatingtables

16. ● Binary or Multiclass logistic regression for classification (labels can have up to 50 unique values)
● K-means clustering for data segmentation (unsupervised learning - not require labels/training)
● Recommend with Matrix factorization
● Model for performing time-series forecasts
● Import TensorFlow models for prediction in BigQuery
● Linear regression for forecasting - the sales of an item on a given day
● Boosted Tree for creating XGBoost | Deep Neural Network DNN models | AutoML tables
Supported models in BigQuery ML
Applying BigQuery ML on E-commerce Data Analytics @martonkodok

17. Conversion/Purchase prediction MODEL: Logistic-Regression
Predict if a user “converts” or "purchases". It is in the company's interest if many users sign up for this
membership as it helps streamline their Ads convertion and also helps with recurring revenue.
Customer Lifetime Value (LTV) prediction. MODEL: Logistic-Regression
It is used by the organisations to identify and prioritizesignificantcustomersegments that would be most
valuable to the company.
Customer Segmentation MODEL: K-means clustering
dividing a client base into groups in specific ways relevanttomarketing, such as interestsandspending
habits. Segmentation allows marketers to better customize their efforts to various audience groups.
E-commerce Use Cases
Applying BigQuery ML on E-commerce Data Analytics @martonkodok

18. Create a MODELthat predicts whether a website visitor will make a transaction.
● CREATEMODEL statement
● TheML.EVALUATE function to evaluate the ML model
● TheML.PREDICTfunction to make predictions using the ML model
Getting started with BigQuery ML
Applying BigQuery ML on E-commerce Data Analytics @martonkodok

19. Create a binarylogisticregressionmodel
Applying BigQuery ML on E-commerce Data Analytics @martonkodok
3
2
Create training dataset
using a labelcolumn
CREATEMODEL syntax
1
2
SELECT features
3
1

20. Evaluate your model
Applying BigQuery ML on E-commerce Data Analytics @martonkodok

21. Predict
Applying BigQuery ML on E-commerce Data Analytics @martonkodok

22. Use cases:
● Customer segmentation
● Data quality
Options and defaults
● Number of clusters: Default log10
(num_rows) clusters
● Distance type - Euclidean(default), Cosine
● Supports all major SQL data types including GIS
K-means clustering
Applying BigQuery ML on E-commerce Data Analytics @martonkodok
CREATE MODEL yourmodel
OPTIONS (model_type = “kmeans”)
AS SELECT..
FROM
ml.PREDICT maps rows to closest clusters
ml.CENTROID for cluster centroids
ml.EVALUATE
ml.TRAINING_INFO
ml.FEATURE_INFO

23. Available data:
● Encode yes/no features
(eg: has a microwave, has a kitchen, has a TV, has a bathroom)
● Can apply clustering on the encoded data
K-means clustering: Problem definition
Applying BigQuery ML on E-commerce Data Analytics @martonkodok

24. Premise
We can identify oddities
(potential data quality issues)
by grouping things together
and separating outliers.
K-means clustering: Problem definition
Applying BigQuery ML on E-commerce Data Analytics @martonkodok

25. Use cases:
● Product recommendation
● Marketing campaign target optimization tool
Options and defaults
● Input: User, Item, Rating
● Can use L2 regularization
● Specify training-test split (default random 80-20)
Matrix Factorization
Applying BigQuery ML on E-commerce Data Analytics @martonkodok
CREATE MODEL yourmodel
OPTIONS (model_type = “matrix_factorization”)
AS SELECT..
FROM
ml.RECOMMEND for full user-item matrix
ml.EVALUATE
ml.WEIGHTS
ml.TRAINING_INFO
ml.FEATURE_INFO

26. Available data:
● User
● Item
● Rating
Problem
● assigning values for previously unknown values
(zeros in our case)
Matrix Factorization: Problem definition
Applying BigQuery ML on E-commerce Data Analytics @martonkodok

27. BigQuery ML - Matrix Factorization
Applying BigQuery ML on E-commerce Data Analytics @martonkodok
CREATE MODEL wr_temp.purchases_mf_model
options(model_type= 'matrix_factorization' )
as
SELECT user,item,rating FROM `wr_temp.purchases`;
SELECT * FROM
ML.RECOMMEND(MODEL wr_temp.purchases_mf_model);
Step 1
Create a model from a dataset.
Step 2
To view the rating associated with a
given user-item pair, use
ML.RECOMMEND with the model name.
The output will return a rating
for each user-item pair.

28. Use cases:
● All sort of time series data forecast
● Marketing campaign target optimization tool
Options and defaults
● Holiday effects adjustments by Region
● Seasonal and trend decomposition
● Auto data frequency detection
Time Series forecasting with ARIMA model
Applying BigQuery ML on E-commerce Data Analytics @martonkodok
CREATE MODEL yourmodel
OPTIONS (model_type = “ARIMA”)
AS SELECT..
ml.FORECAST to be use with HORIZON
ml.EVALUATE
ml.ARIMA_COEFFICIENTS

29. Available data:
● Past Timestamp
● Past Value
Problem
● Forecasts for next X slots (called horizon)
Time Series forecasting with ARIMA model
Applying BigQuery ML on E-commerce Data Analytics @martonkodok
SELECT forecast_timestamp, forecast_value FROM
ML.FORECAST(MODEL bqml_tutorial.nyc_citibike_arima_model,
STRUCT(300 AS horizon, 0.8 AS confidence_level))

30. Use cases:
● Easily add TensorFlow predictions to BigQuery
● Build unstructured data models in TensorFlow,
predict in BigQuery
Key restrictions
● Model size limit of 250MB
Import TensorFlow models for prediction
Applying BigQuery ML on E-commerce Data Analytics @martonkodok
CREATE MODEL yourmodel
OPTIONS (model_type =“tensorflow”,
Model_path =’gs://’)
ml.PREDICT()
DEMO
Search 'QueryIt Smart' on GitHub to learn more.

31. Google Drive - Collaboratory - Jupyter Notebook
Applying BigQuery ML on E-commerce Data Analytics @martonkodok

32. New on BigQuery UI - Evaluation charts
Applying BigQuery ML on E-commerce Data Analytics @martonkodok

33. Conclusions
Applying BigQuery ML on E-commerce Data Analytics @martonkodok

34. Automation
● Run the process daily
● Determine hyperparameters
● Surface the results and route them somewhere for inspection and improvement
Testing
● AB test around impact of data quality on conversion and customer NPS (net promoter score)
Improvements
● Determine, and explore outliers
● Repeat, automate
Considerations
Applying BigQuery ML on E-commerce Data Analytics @martonkodok

35. ● Democratizes the use of ML by empowering data analysts to build and run models using existing
business intelligence tools and spreadsheets
● Generalist team. Models are trained using SQL. There is no need to program an ML solution using
Python or Java.
● Increases the innovation and speed of model development by removing the need to export data from
the data warehouse.
● A Model serves a purpose. Easy to change/recycle.
Benefits of BigQuery ML
Applying BigQuery ML on E-commerce Data Analytics @martonkodok

36. The possibilities are endless
Applying BigQuery ML on E-commerce Data Analytics @martonkodok
Marketing Retail IndustrialandIoT Media/gaming
Predict customer value
Predict funnel conversion
Personalize ads, email,
webpage content
Optimize inventory
Forecast revenue
Enable product
recommendations
Optimize staff promotions
Forecast demand for
parking, traffic utilities,
personnel
Prevent equipment
downtime
Predict maintenance needs
Personalize content
Predict game difficulty
Predict player lifetime value

37. Thank you.
Slides available on:
slideshare.net/martonkodok
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